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# coding: utf-8
#/*##########################################################################
# Copyright (C) 2016-2017 European Synchrotron Radiation Facility
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in
# all copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
# THE SOFTWARE.
#
#############################################################################*/
"""This module provides a peak search function and tools related to peak
analysis.
"""
__authors__ = ["P. Knobel"]
__license__ = "MIT"
__date__ = "17/06/2016"
import logging
import numpy
from . import filters
logging.basicConfig()
_logger = logging.getLogger(__name__)
cimport cython
from libc.stdlib cimport free
cimport peaks_wrapper
def peak_search(y, fwhm, sensitivity=3.5,
begin_index=None, end_index=None,
debug=False, relevance_info=False):
"""Find peaks in a curve.
:param y: Data array
:type y: numpy.ndarray
:param fwhm: Estimated full width at half maximum of the typical peaks we
are interested in (expressed in number of samples)
:param sensitivity: Threshold factor used for peak detection. Only peaks
with amplitudes higher than ``σ * sensitivity`` - where ``σ`` is the
standard deviation of the noise - qualify as peaks.
:param begin_index: Index of the first sample of the region of interest
in the ``y`` array. If ``None``, start from the first sample.
:param end_index: Index of the last sample of the region of interest in
the ``y`` array. If ``None``, process until the last sample.
:param debug: If ``True``, print debug messages. Default: ``False``
:param relevance_info: If ``True``, add a second dimension with relevance
information to the output array. Default: ``False``
:return: 1D sequence with indices of peaks in the data
if ``relevance_info`` is ``False``.
Else, sequence of ``(peak_index, peak_relevance)`` tuples (one tuple
per peak).
:raise: ``IndexError`` if the number of peaks is too large to fit in the
output array.
"""
cdef:
int i
double[::1] y_c
double* peaks_c
double* relevances_c
y_c = numpy.array(y,
copy=True,
dtype=numpy.float64,
order='C').reshape(-1)
if debug:
debug = 1
else:
debug = 0
if begin_index is None:
begin_index = 0
if end_index is None:
end_index = y_c.size - 1
n_peaks = peaks_wrapper.seek(begin_index, end_index, y_c.size,
fwhm, sensitivity, debug,
&y_c[0], &peaks_c, &relevances_c)
# A negative return value means that peaks were found but not enough
# memory could be allocated for all
if n_peaks < 0 and n_peaks != -123456:
msg = "Before memory allocation error happened, "
msg += "we found %d peaks.\n" % abs(n_peaks)
_logger.debug(msg)
msg = ""
for i in range(abs(n_peaks)):
msg += "peak index %f, " % peaks_c[i]
msg += "relevance %f\n" % relevances_c[i]
_logger.debug(msg)
free(peaks_c)
free(relevances_c)
raise MemoryError("Failed to reallocate memory for output arrays")
# Special value -123456 is returned if the initial memory allocation
# fails, before any search could be performed
elif n_peaks == -123456:
raise MemoryError("Failed to allocate initial memory for " +
"output arrays")
peaks = numpy.empty(shape=(n_peaks,),
dtype=numpy.float64)
relevances = numpy.empty(shape=(n_peaks,),
dtype=numpy.float64)
for i in range(n_peaks):
peaks[i] = peaks_c[i]
relevances[i] = relevances_c[i]
free(peaks_c)
free(relevances_c)
if not relevance_info:
return peaks
else:
return list(zip(peaks, relevances))
def guess_fwhm(y):
"""Return the full-width at half maximum for the largest peak in
the data array.
The algorithm removes the background, then finds a global maximum
and its corresponding FWHM.
This value can be used as an initial fit parameter, used as input for
an iterative fit function.
:param y: Data to be used for guessing the fwhm.
:return: Estimation of full-width at half maximum, based on fwhm of
the global maximum.
"""
# set at a minimum value for the fwhm
fwhm_min = 4
# remove data background (computed with a strip filter)
background = filters.strip(y, w=1, niterations=1000)
yfit = y - background
# basic peak search: find the global maximum
maximum = max(yfit)
# find indices of all values == maximum
idx = numpy.nonzero(yfit == maximum)[0]
# take the last one (if any)
if not len(idx):
return 0
posindex = idx[-1]
height = yfit[posindex]
# now find the width of the peak at half maximum
imin = posindex
while yfit[imin] > 0.5 * height and imin > 0:
imin -= 1
imax = posindex
while yfit[imax] > 0.5 * height and imax < len(yfit) - 1:
imax += 1
fwhm = max(imax - imin - 1, fwhm_min)
return fwhm
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